一句话：提出金属嵌入法将LLM权重硬连线到三维金属线拓扑，使HNLPU专用推理芯片在可行光罩成本下实现极高能效与性价比。
LLM推理能耗高，专用LPU需硬连线权重以求极致效率，但现代LLM规模使直接硬连线的光罩成本超60亿美元。本文提出HNLPU与Metal-Embedding：将权重嵌入金属线三维拓扑而非硅二维阵列，使密度提升15倍，70层光罩中60层可跨芯片同构（含EUV），光罩成本降低112倍，NRE可行。HNLPU达249,960 tokens/s、36 tokens/J，5 nm下NRE约59.46M–123.5M美元；相比H100集群，性价比提升41.7–80.4×，碳足迹降低357倍。
The rapid advancement of Large Language Models (LLMs) has established language as a core general-purpose cognitive substrate, driving the demand for specialized Language Processing Units (LPUs) tailored for LLM inference. To overcome the growing energy consumption of LLM inference systems, this paper proposes a Hardwired-Neurons Language Processing Unit (HNLPU), which physically hardwires LLM weight parameters into the computational fabric, achieving several orders of magnitude computational efficiency improvement by extreme specialization. However, a significant challenge still lies in the scale of modern LLMs. A straightforward hardwiring of GPT-OSS-120B would require fabricating photomask sets valued at over 6 billion dollars, rendering this straightforward solution economically impractical. Addressing this challenge, we propose the novel Metal-Embedding methodology. Instead of embedding weights in a 2D grid of silicon device cells, Metal-Embedding embeds weight parameters into the 3D topology of metal wires. This brings two benefits: (1) a 15× increase in density, and (2) 60 out of 70 photomask layers are homogeneous across chips, including all EUV photomasks. In total, Metal-Embedding reduced the photomask cost by 112×, bringing the Non-Recurring Engineering (NRE) cost of HNLPU into an economically viable range. Experimental results show that HNLPU achieved 249,960 tokens/s (5,555×/85× that of GPU/WSE), 36 tokens/J (1,047×/283× that of GPU/WSE), 13,232 mm2 total die area, 59.46 M-123.5 M estimated NRE at 5 nm technology. Analysis shows that HNLPU achieved 41.7-80.4× improvement in cost-effectiveness and 357× reduction in carbon footprint compared to OpenAI-scale H100 clusters, under an annual weight updating assumption.
@inproceedings{LiuCZ0ZKLJXMLWL26,
title = {Hardwired-Neuron Language Processing Units as General-Purpose Cognitive Substrates},
author = {Yang Liu and Yi Chen and Yongwei Zhao and Yifan Hao and Zifu Zheng and Weihao Kong and Zhangmai Li and Dongchen Jiang and Ruiyang Xia and Zhihong Ma and Zisheng Liu and Zhaoyong Wan and Yunqi Lu and Ximing Liu and Hongrui Guo and Zhihao Yang and Zhe Wang and Tianrui Ma and Mo Zou and Rui Zhang and Ling Li and Xing Hu and Zidong Du and Zhiwei Xu and Qi Guo and Tianshi Chen and Yunji Chen},
booktitle = {ASPLOS},
year = {2026},
doi = {10.1145/3779212.3790169}
}